Cognitive Neuroscience
Faculty of Biology and Biotechnology
Ruhr University Bochum
Universitätsstr. 150
44801 Bochum
Room:
NDEF 05/348, Fach 59
Phone:
+49 (0)234 32-15220
Email:
caspar.schwiedrzik@ruhr-uni-bochum.de
Our environment constantly changes, posing ever new challenges to the health and wellbeing of the organisms it harbors, with profound consequences for how we live and interact. One important component in understanding the interaction between us and our environment is understanding the processes that allow individuals to adjust to new environmental challenges: to remain optimally adapted, we and other animals rely upon our cognitive and behavioral flexibility. In particular, we require mechanisms for the recognition of newly arising environmental challenges, be they perceptual, cognitive, or social, as well as the ability to rapidly attune ourselves to and anticipate these challenges. In other words: learning. In my lab, we investigate the neural basis of learning mechanisms and their flexibility, taking a comparative perspective. To this end, we employ a multi-modal approach including electrophysiological and neuroimaging techniques in humans and other animals, as well as computer models to identify general principles and/or species-specific neural solutions to the problem of learning. We focus on the visual system, where the complex problem of learning can be broken down into tractable hypotheses. In doing so, we chart the brain’s capacities to adapt to change, as well as the limits of adaptability and the transition to maladaptive states - a necessary step towards understanding the human mind and its complexity.
Ivanov, V., Manenti, G. L., Plewe, S. S., Kagan, I., & Schwiedrzik, C. M. (2024). Decision-making processes in perceptual learning depend on effectors. Scientific Reports, 14(1), 5644. https://doi.org/10.1038/s41598-024-55508-5
Karami, B., & Schwiedrzik, C. M. (2024). Visual perceptual learning of feature conjunctions leverages non-linear mixed selectivity. NPJ Science of Learning, 9(1), 13. https://doi.org/10.1038/s41539-024-00226-w
Nigam, T., & Schwiedrzik, C. M. (2024). Predictions enable top-down pattern separation in the macaque face-processing hierarchy. Nature Communications, 15(1), 7196. https://doi.org/10.1038/s41467-024-51543-y
Deen, B., Schwiedrzik, C. M., Sliwa, J., & Freiwald, W. A. (2023). Specialized Networks for Social Cognition in the Primate Brain. Annual Review of Neuroscience, 46, 381–401. https://doi.org/10.1146/annurev-neuro-102522-121410
Manenti, G. L., Dizaji, A. S., & Schwiedrzik, C. M. (2023). Variability in training unlocks generalization in visual perceptual learning through invariant representations. Current Biology : CB, 33(5), 817-826.e3. https://doi.org/10.1016/j.cub.2023.01.011
Liashenko, A., Dizaji, A. S., Melloni, L., & Schwiedrzik, C. M. (2020). Memory guidance of value-based decision making at an abstract level of representation. Scientific Reports, 10(1), 21496. https://doi.org/10.1038/s41598-020-78460-6
Schwiedrzik, C. M., & Sudmann, S. S. (2020). Pupil Diameter Tracks Statistical Structure in the Environment to Increase Visual Sensitivity. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 40(23), 4565–4575. https://doi.org/10.1523/JNEUROSCI.0216-20.2020
Schwiedrzik, C. M., Sudmann, S. S., Thesen, T., Wang, X., Groppe, D. M., Mégevand, P., Doyle, W., Mehta, A. D., Devinsky, O., & Melloni, L. (2018). Medial prefrontal cortex supports perceptual memory. Current Biology : CB, 28(18), R1094-R1095. https://doi.org/10.1016/j.cub.2018.07.066
Schwiedrzik, C. M., & Freiwald, W. A. (2017). High-Level Prediction Signals in a Low-Level Area of the Macaque Face-Processing Hierarchy. Neuron, 96(1), 89-97.e4. https://doi.org/10.1016/j.neuron.2017.09.007
Schwiedrzik, C. M., Zarco, W., Everling, S., & Freiwald, W. A. (2015). Face Patch Resting State Networks Link Face Processing to Social Cognition. PLoS Biology, 13(9), e1002245. https://doi.org/10.1371/journal.pbio.1002245
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Last update: Oct 21, 2024
